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fix name_scope default_name. · ShuchenQ/TensorFlow.NET@649623b · GitHub

/ TensorFlow.NET Public
forked from SciSharp/TensorFlow.NET

Commit 649623b

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Oceania2018
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fix name_scope default_name.
1 parent 8dfc3b7 commit 649623b

7 files changed

Lines changed: 43 additions & 31 deletions

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‎data/linear_regression.zip‎

3.34 KB
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‎src/TensorFlowNET.Core/Operations/OpDefLibrary.cs‎

Lines changed: 3 additions & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -193,7 +193,9 @@ public Operation _apply_op_helper(string op_type_name, string name = "", dynamic
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}
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// Add Op to graph
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var op = g.create_op(op_type_name, inputs.ToArray(), output_types.ToArray(),
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var op = g.create_op(op_type_name,
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inputs.ToArray(),
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output_types.ToArray(),
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name: scope,
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input_types: input_types.ToArray(),
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attrs: attr_protos,

‎src/TensorFlowNET.Core/Train/Saving/BaseSaverBuilder.cs‎

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -61,7 +61,7 @@ public virtual SaverDef _build_internal(RefVariable[] names_to_saveables,
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bool sharded = false,
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int max_to_keep = 5,
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float keep_checkpoint_every_n_hours = 10000,
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string name = "",
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string name = null,
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bool restore_sequentially = false,
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string filename = "model",
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bool build_save = true,

‎src/TensorFlowNET.Core/Train/Saving/Saver.cs‎

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -37,7 +37,7 @@ public Saver(RefVariable[] var_list = null,
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bool sharded = false,
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int max_to_keep = 5,
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float keep_checkpoint_every_n_hours = 10000,
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string name = "",
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string name = null,
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bool restore_sequentially = false,
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SaverDef saver_def = null,
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ISaverBuilder builder = null,

‎src/TensorFlowNET.Core/ops.name_scope.cs‎

Lines changed: 1 addition & 4 deletions
Original file line numberDiff line numberDiff line change
@@ -27,10 +27,7 @@ public name_scope(string name, string default_name = "", object values = null)
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public void __enter__()
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{
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if (String.IsNullOrEmpty(_name))
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{
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_name = _default_name;
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}
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_name = _name == null ? _default_name : _name;
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Graph g = null;
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if (_values is List<Tensor> values)

‎test/TensorFlowNET.Examples/LinearRegression.cs‎

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -57,7 +57,7 @@ public void Run()
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var grad = tf.train.GradientDescentOptimizer(learning_rate);
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var optimizer = grad.minimize(cost);*/
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var new_saver = tf.train.import_meta_graph("save_model.meta", import_scope: "import");
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var new_saver = tf.train.import_meta_graph("linear_regression.meta");
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var X = graph.OperationByName("Placeholder");
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var Y = graph.OperationByName("Placeholder_1");

‎test/TensorFlowNET.Examples/python/linear_regression.py‎

Lines changed: 36 additions & 23 deletions
Original file line numberDiff line numberDiff line change
@@ -14,7 +14,7 @@
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# Parameters
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learning_rate = 0.01
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training_epochs = 1000
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display_step = 50
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display_step = 10
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# Training Data
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train_X = numpy.asarray([3.3,4.4,5.5,6.71,6.93,4.168,9.779,6.182,7.59,2.167,
@@ -23,28 +23,41 @@
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2.827,3.465,1.65,2.904,2.42,2.94,1.3])
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n_samples = train_X.shape[0]
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# tf Graph Input
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X = tf.placeholder("float")
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Y = tf.placeholder("float")
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# Set model weights
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W = tf.Variable(rng.randn(), name="weight")
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b = tf.Variable(rng.randn(), name="bias")
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# Construct a linear model
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mul = tf.multiply(X, W)
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pred = tf.add(mul, b)
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# Mean squared error
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sub = pred-Y
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pow = tf.pow(sub, 2)
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reduce = tf.reduce_sum(pow)
43-
cost = reduce/(2*n_samples)
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# Gradient descent
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# Note, minimize() knows to modify W and b because Variable objects are trainable=True by default
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grad = tf.train.GradientDescentOptimizer(learning_rate)
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optimizer = grad.minimize(cost)
26+
if False:
27+
# tf Graph Input
28+
X = tf.placeholder("float")
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Y = tf.placeholder("float")
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# Set model weights
32+
W = tf.Variable(-0.06, name="weight")
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b = tf.Variable(-0.73, name="bias")
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# Construct a linear model
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mul = tf.multiply(X, W)
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pred = tf.add(mul, b)
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# Mean squared error
40+
sub = pred-Y
41+
pow = tf.pow(sub, 2)
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reduce = tf.reduce_sum(pow)
44+
cost = reduce/(2*n_samples)
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# Gradient descent
46+
# Note, minimize() knows to modify W and b because Variable objects are trainable=True by default
47+
grad = tf.train.GradientDescentOptimizer(learning_rate)
48+
optimizer = grad.minimize(cost)
49+
# tf.train.export_meta_graph(filename='save_model.meta');
50+
else:
51+
# tf Graph Input
52+
new_saver = tf.train.import_meta_graph("save_model.meta")
53+
nodes = tf.get_default_graph()._nodes_by_name;
54+
optimizer = nodes["GradientDescent"]
55+
cost = nodes["truediv"].outputs[0]
56+
X = nodes["Placeholder"].outputs[0]
57+
Y = nodes["Placeholder_1"].outputs[0]
58+
W = nodes["weight"].outputs[0]
59+
b = nodes["bias"].outputs[0]
60+
pred = nodes["Add"].outputs[0]
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4962
# Initialize the variables (i.e. assign their default value)
5063
init = tf.global_variables_initializer()

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